// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#include "main.h"
#include <Eigen/CXX11/Tensor>
#include <limits>
#include <numeric>

using Eigen::Tensor;

template<int DataLayout, typename Type = float, bool Exclusive = false>
static void
test_1d_scan()
{
	int size = 50;
	Tensor<Type, 1, DataLayout> tensor(size);
	tensor.setRandom();
	Tensor<Type, 1, DataLayout> result = tensor.cumsum(0, Exclusive);

	VERIFY_IS_EQUAL(tensor.dimension(0), result.dimension(0));

	float accum = 0;
	for (int i = 0; i < size; i++) {
		if (Exclusive) {
			VERIFY_IS_EQUAL(result(i), accum);
			accum += tensor(i);
		} else {
			accum += tensor(i);
			VERIFY_IS_EQUAL(result(i), accum);
		}
	}

	accum = 1;
	result = tensor.cumprod(0, Exclusive);
	for (int i = 0; i < size; i++) {
		if (Exclusive) {
			VERIFY_IS_EQUAL(result(i), accum);
			accum *= tensor(i);
		} else {
			accum *= tensor(i);
			VERIFY_IS_EQUAL(result(i), accum);
		}
	}
}

template<int DataLayout, typename Type = float>
static void
test_4d_scan()
{
	int size = 5;
	Tensor<Type, 4, DataLayout> tensor(size, size, size, size);
	tensor.setRandom();

	Tensor<Type, 4, DataLayout> result(size, size, size, size);

	result = tensor.cumsum(0);
	float accum = 0;
	for (int i = 0; i < size; i++) {
		accum += tensor(i, 1, 2, 3);
		VERIFY_IS_EQUAL(result(i, 1, 2, 3), accum);
	}
	result = tensor.cumsum(1);
	accum = 0;
	for (int i = 0; i < size; i++) {
		accum += tensor(1, i, 2, 3);
		VERIFY_IS_EQUAL(result(1, i, 2, 3), accum);
	}
	result = tensor.cumsum(2);
	accum = 0;
	for (int i = 0; i < size; i++) {
		accum += tensor(1, 2, i, 3);
		VERIFY_IS_EQUAL(result(1, 2, i, 3), accum);
	}
	result = tensor.cumsum(3);
	accum = 0;
	for (int i = 0; i < size; i++) {
		accum += tensor(1, 2, 3, i);
		VERIFY_IS_EQUAL(result(1, 2, 3, i), accum);
	}
}

template<int DataLayout>
static void
test_tensor_maps()
{
	int inputs[20];
	TensorMap<Tensor<int, 1, DataLayout>> tensor_map(inputs, 20);
	tensor_map.setRandom();

	Tensor<int, 1, DataLayout> result = tensor_map.cumsum(0);

	int accum = 0;
	for (int i = 0; i < 20; ++i) {
		accum += tensor_map(i);
		VERIFY_IS_EQUAL(result(i), accum);
	}
}

EIGEN_DECLARE_TEST(cxx11_tensor_scan)
{
	CALL_SUBTEST((test_1d_scan<ColMajor, float, true>()));
	CALL_SUBTEST((test_1d_scan<ColMajor, float, false>()));
	CALL_SUBTEST((test_1d_scan<RowMajor, float, true>()));
	CALL_SUBTEST((test_1d_scan<RowMajor, float, false>()));
	CALL_SUBTEST(test_4d_scan<ColMajor>());
	CALL_SUBTEST(test_4d_scan<RowMajor>());
	CALL_SUBTEST(test_tensor_maps<ColMajor>());
	CALL_SUBTEST(test_tensor_maps<RowMajor>());
}
